Enterprise-grade Vector Database Solutions by SyneHQ

This Dockerfile creates a production-ready PostgreSQL database with pgvector extension enabled for vector similarity search capabilities. It also includes pg_stat_statements extension for query performance monitoring, developed and maintained by SyneHQ.

📢 Looking for a standard PostgreSQL image? Check out our synehq/pg image for non-vector database requirements! Same enterprise-grade quality without the vector extensions. Perfect for traditional database workloads! 🐘

• 🏢 Enterprise-ready vector database solution
• 🐘 Based on official pgvector/pgvector:pg17 image
• 📊 Includes pg_stat_statements extension for query performance monitoring
• 🔍 Configured with vector similarity search support
• 🔌 Increased max connections (200)
• 🧙‍♂️ Automatic initialization with vector extensions and sample table
• 🔮 Pre-configured similarity search function
• 🚄 Production-grade performance optimizations

docker run -d --name postgres-pgvector \
    -e POSTGRES_PASSWORD=your_password \
    -p 5432:5432 \
    -v $(pwd)/certificates:/etc/postgresql/ssl \
    ghcr.io/synehq/pg-vector

docker run -d --name postgres-pg \
    -e POSTGRES_PASSWORD=your_password \
    -p 5432:5432 \
    -v $(pwd)/certificates:/etc/postgresql/ssl \
    ghcr.io/synehq/pg

docker build -t synehq/pg-vector vector/

docker build -t synehq/pg .

Need some certificates but don't want the hassle? We've got you covered! 🛡️

Check this article here

This approach provides secure ways to pass SSL certificates to your PostgreSQL container, ensuring proper encryption for your database connections while maintaining security best practices.

docker run -d --name postgres-pgvector \
    -e POSTGRES_PASSWORD=your_password \
    -p 5432:5432 \
    -v $(pwd)/certificates:/etc/postgresql/ssl \
    ghcr.io/synehq/pg-vector

docker run -d --name postgres-pg \
    -e POSTGRES_PASSWORD=your_password \
    -v $(pwd)/certificates:/etc/postgresql/ssl \
    -p 5432:5432 \
    ghcr.io/synehq/pg

docker exec -it postgres-pgvector psql -U postgres

The container automatically:

• 🔌 Enables the vector and pg_stat_statements extensions
• 📋 Creates a sample 'items' table with vector embedding support
• 🚀 Sets up an IVFFlat index for efficient similarity search
• 🧪 Creates a helper function for vector similarity matching

-- Insert a sample item (it's that easy! 🎉)
INSERT INTO items (description, embedding) 
VALUES ('Sample item', '[0.1, 0.2, ..., 0.5]'::vector);

-- Find similar items (like magic! 🪄)
SELECT * FROM match_items(
    '[0.1, 0.2, ..., 0.5]'::vector,  -- query embedding
    0.8,                             -- similarity threshold
    5                                -- number of results
);

• id: Serial primary key
• description: Text description (tell us your stories! 📝)
• embedding: Vector(1536) for storing embeddings (where the magic happens! ✨)
• created_at: Timestamp of creation (we like to keep track of time! ⏰)

Finds similar items based on vector similarity (like finding your database soulmates! 💘):

• query_embedding: Vector to match against
• match_threshold: Minimum similarity score (0-1)
• match_count: Maximum number of results to return

Choose the right tool for your job! Both images include our enterprise-grade optimizations and support. 🛠️

• Documentation 📖
• API Reference 🔌
• Community Forum 👥
• Enterprise Support 🏢

synehq provides enterprise-grade database solutions for modern AI applications. Our technology powers thousands of production deployments worldwide, helping businesses leverage the power of vector similarity search at scale. We're like database superheros, but without the capes! 🦸‍♂️🦸‍♀️

This project is licensed under the MIT License - see synehq.com/license for details. No fine print, we promise! 😉